Abstract Body (Do not enter title and authors here): Background: clarity in the molecular mechanisms contributing to pre-clinical stages of coronary atherosclerosis is needed to develop novel early diagnostic tools and more effective preventive interventions.
Methods: We completed untargeted proteomic and RNA expression analysis on proximal coronary arterial (CA) tissues from young adults who died of trauma with no antemortem suspicion of coronary disease (n=322, mean age (range): 34.1 yrs. (15-59); sex: M-239, F-83; race: W-218, B-88, other-16), including n=180 (56%) with morphologic evidence of pre-clinical atherosclerosis. Proteomic data (n=1900 proteins) were analyzed using manifold learning and unsupervised latent feature (LF) detection methods to define the pseudo-temporal progression of disease from normal to advanced atherosclerosis and to identify molecular drivers of disease initiation and progression. Correlation and network mapping of the mRNA data (n=16,707 transcripts, including n=1707 annotated transcription factors [TFs]) from the same arterial samples was used to define the transcriptional regulatory networks (TRNs) associated with the observed proteomic LF dynamics.
Results: Four of seven identified LFs had a significant (FDR Conclusions: The pseudotemporal pattern of the SMC LF was consistent with SMC phenotype changes as early initiating events in atherosclerosis of the CA. Therapies to promote or inhibit activity of these identified TFs may prevent against early lesion formation and atherosclerosis progression by supporting SMC phenotype stability.